Matrices for engineers
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiobject Behavior Recognition by Event Driven Selective Attention Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Maximum-Likelihood Strategy for Directing Attention during Visual Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data- and Model-Driven Gaze Control for an Active-Vision System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Focus-of-Attention from Local Color Symmetries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Gesture Recognition by Learning and Selective Control of Visual Interest Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Parsing: Unifying Segmentation, Detection, and Recognition
International Journal of Computer Vision
Combinatorial Algorithms: Theory and Practice
Combinatorial Algorithms: Theory and Practice
Attention-Based Dynamic Visual Search Using Inner-Scene Similarity: Algorithms and Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Attention-driven image interpretation with application to image retrieval
Pattern Recognition
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A generic framework of user attention model and its application in video summarization
IEEE Transactions on Multimedia
Visual sensitivity guided bit allocation for video coding
IEEE Transactions on Multimedia
A minimum entropy approach to adaptive image polygonization
IEEE Transactions on Image Processing
Automatic foveation for video compression using a neurobiological model of visual attention
IEEE Transactions on Image Processing
Tree structures with attentive objects for image classification using a neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Expert Systems with Applications: An International Journal
A critical review of selective attention: an interdisciplinary perspective
Artificial Intelligence Review
Semantic context based refinement for news video annotation
Multimedia Tools and Applications
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In the attention-driven image interpretation process, an image is interpreted as containing several perceptually attended objects as well as the background. The process benefits greatly a content-based image retrieval task with attentively important objects identified and emphasized. An important issue to be addressed in an attention-driven image interpretation is to reconstruct several attentive objects iteratively from the segments of an image by maximizing a global attention function. The object reconstruction is a combinational optimization problem with a complexity of 2^N which is computationally very expensive when the number of segments N is large. In this paper, we formulate the attention-driven image interpretation process by a matrix representation. An efficient algorithm based on the elementary transformation of matrix is proposed to reduce the computational complexity to 3@wN(N-1)^2/2, where @w is the number of runs. Experimental results on both the synthetic and real data show a significantly improved processing speed with an acceptable degradation to the accuracy of object formulation.